{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n",
    "train_images = (train_images - 127.5)/127.5\n",
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 256\n",
    "BUFFER_SIZE = 60000\n",
    "noise_dim = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: ((28, 28, 1), ()), types: (tf.float32, tf.uint8)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n",
    "#datasets #<TensorSliceDataset shapes: (28, 28), types: tf.float32>\n",
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = datasets.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_model(): #生成器\n",
    "    \n",
    "    seed = layers.Input(shape=((noise_dim,)))\n",
    "    label = layers.Input(shape=(()))\n",
    "    \n",
    "    x = layers.Embedding(10, noise_dim, input_length=1)(label) #0-9长度为10，输入序列维数为1\n",
    "    x = layers.concatenate([seed, x])\n",
    "    x = layers.Dense(3*3*128, use_bias=False)(x)\n",
    "    x = layers.Reshape((3, 3, 128))(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.ReLU()(x)  \n",
    "    \n",
    "    x = layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='valid', use_bias=False)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.ReLU()(x)             #7*7*64\n",
    "    \n",
    "    x = layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.ReLU()(x)             #14*14*32\n",
    "    \n",
    "    x = layers.Conv2DTranspose(1, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "    x = layers.Activation('tanh')(x)                                 #28*28*1\n",
    "    \n",
    "    model = tf.keras.Model(inputs=[seed, label], outputs=x)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen=generator_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_model():\n",
    "    image = tf.keras.Input(shape=((28,28,1)))\n",
    "    label = tf.keras.Input(shape=(()))\n",
    "    \n",
    "    x = layers.Embedding(10, 28*28, input_length=1)(label)\n",
    "    x = layers.Reshape((28, 28, 1))(x)\n",
    "    x = layers.concatenate([image, x])\n",
    "    \n",
    "    x = layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.LeakyReLU()(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    \n",
    "    x = layers.Conv2D(32*2, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.LeakyReLU()(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    \n",
    "    x = layers.Conv2D(32*4, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.LeakyReLU()(x)\n",
    "    x = layers.Dropout(0.5)(x)\n",
    "    \n",
    "    x = layers.Flatten()(x)\n",
    "    x1 = layers.Dense(1)(x)\n",
    "    \n",
    "    model = tf.keras.Model(inputs=[image, label], outputs=x1)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_loss(real_out, fake_out):\n",
    "    real_loss = cross_entropy(tf.ones_like(real_out), real_out)\n",
    "    fake_loss = cross_entropy(tf.zeros_like(fake_out), fake_out)\n",
    "    return real_loss + fake_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator_loss(fake_out):\n",
    "    return cross_entropy(tf.ones_like(fake_out), fake_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator_opt = tf.keras.optimizers.Adam(1e-4)\n",
    "discriminator_opt = tf.keras.optimizers.Adam(1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = generator_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = discriminator_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_step(images, labels):\n",
    "    batchsize = labels.shape[0]\n",
    "    noise = tf.random.normal([batchsize, noise_dim]) #当数据最后一个批次不足BATCH_SIZE时，用此方法可解决\n",
    "    \n",
    "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
    "        real_out = discriminator((images, labels), training=True)\n",
    "        \n",
    "        gen_images = generator((noise, labels), training=True)\n",
    "        fake_out = discriminator((gen_images, labels), training=True)\n",
    "        \n",
    "        gen_loss = generator_loss(fake_out)\n",
    "        disc_loss = discriminator_loss(real_out, fake_out)\n",
    "        \n",
    "    gradient_gen = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
    "    gradient_disc = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
    "    generator_opt.apply_gradients(zip(gradient_gen, generator.trainable_variables))\n",
    "    discriminator_opt.apply_gradients(zip(gradient_disc, discriminator.trainable_variables))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_dim = 50 #输入的噪声向量维数（要与之前生产器模型的输入相对应）\n",
    "\n",
    "num = 10 #每个EPOCH生产16张图片查看\n",
    "\n",
    "noise_seed = tf.random.normal([num, noise_dim])\n",
    "cat_seed = np.random.randint(0, 10, size=(num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_plot_image(gen_model, test_noise, label, epoch_num):\n",
    "    print('Epoch:', epoch_num+1)\n",
    "    pre_images = gen_model((test_noise, label), training=False)\n",
    "    pre_images = tf.squeeze(pre_images)  #(None, 28, 28, 1)——>(None, 28, 28) plt.imshow((pre_images[i, :, :, 0]+1)/2, cmap='gray')\n",
    "    fig = plt.figure(figsize=(10, 1))\n",
    "    for i in range(pre_images.shape[0]):\n",
    "        plt.subplot(1, 10, i+1)\n",
    "        plt.imshow((pre_images[i, :, : ]+1)/2, cmap='gray')\n",
    "        plt.axis('off')        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(dataset, epochs):\n",
    "    for epoch in range(epochs):\n",
    "        for image_batch, label_batch in dataset:\n",
    "            train_step(image_batch, label_batch)\n",
    "        if epoch % 10 == 0:\n",
    "            print('.', end='')\n",
    "            generate_plot_image(generator, noise_seed, cat_seed, epoch)\n",
    "    generate_plot_image(generator, noise_seed, cat_seed, epoch)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".Epoch: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".Epoch: 11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".Epoch: 21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".Epoch: 31\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".Epoch: 41\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train(datasets, EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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